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SUMMARY:Algorithmic Trading with Learning: Informed versus Uninformed - Ca
 rtea\, A (University College London)
DTSTART:20131120T115000Z
DTEND:20131120T124000Z
UID:TALK48914@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:High-frequency traders often take a view on the market and the
 n act accordingly: buy an asset if they predict an upward trend or sell an
  asset if they expect a downward trend. However\, if they are not fully co
 nfident in their prediction\, how can they optimally trade? Here\, we deve
 lop a framework to address this problem by first modeling the asset mid-pr
 ice with a randomized Brownian bridge. The randomization encodes the trade
 r's prior estimate of the asset's future midprice distribution\, e.g.\, a 
 two point discrete random variable corresponds to upward/downward movement
 s. We pose and solve the optimal control and stopping problem for how the 
 trader should post limit orders at the touch and/or cross the spread and e
 xecute market orders. The optimal trading strategy indeed learns from the 
 dynamics of the asset's midprice which trend is being realized and modifie
 s its behavior accordingly. By comparing the performance three traders who
  differ in the accuracy of their predictions and whether they learn or not
 \, we demonstrate that traders can significantly benefit from using our ap
 proach. \n\nAuthors: Alvaro Cartea\, Ryan Donnelly\, Sebastian Jaimungal \
 n
LOCATION:Seminar Room 2\, Newton Institute Gatehouse
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